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Senior ML Engineer

What you'll do:

  • Serve as the crucial link and central figure between research and engineering teams.
  • Engage in deep collaboration with researchers to transform research insights into broader applications.
  • Tackle intricate challenges with scalable, straightforward solutions from initial proof of concept to production deployment.
  • Drive the development of groundbreaking technology solutions, expanding the limits of our technical capabilities.
  • Excel as a machine learning authority: Master the technologies behind the training, fine-tuning, and deployment of the most advanced models. You’ll work on the inner workings of models, focusing on crafting robust security solutions for them.

 

Who we're looking for:

  • A remarkable individual who balances a strong footing in both software engineering and ML development realms.
  • Expertise in transforming algorithmic prototypes into market-ready products, directly influencing the company's growth.
  • A passionate enthusiast in reverse engineering: Proficient in dissecting complex systems to derive meaningful insights.
  • A proficient coder: Versatile in various programming languages, devising elegant, problem-solving software solutions - who’s extremely proficient in Python.
  • A dynamic team collaborator: Known for effective teamwork, open knowledge sharing, and excelling in a dynamic environment.
  • Capable of conveying intricate systems and features with both depth and clarity.
  • Daily engagement with cutting-edge fields: machine learning, big data, and cloud computing.
  • Minimum 5 years of practical experience in developing machine learning and statistical modeling solutions.

 

Qualifications:

  • Master’s degree in Computer Science/Software Engineering or substantial practical experience with a bachelor's degree in a STEM subject.
  • An advocate for innovation and advanced technology, continually exploring the realm of possibilities.
  • Proficient in Python programming, design patterns and knows strict typing languages.
  • Experienced in data science tools (e.g. PyTorch, TensorFlow, Pandas, scikit-learn, etc).
  • Skilled in adapting machine learning techniques to optimize for modern parallel computing environments (including distributed clusters, multicore SMP, and GPU).
  • In-depth knowledge and hands-on experience with at least one type of ML model (vision, NLP, voice, etc) encompassing training, fine-tuning, and their applications.
  • Solid experience in MLOps, adept at designing and managing expansive ML infrastructures.
  • Possess strong problem solving and critical thinking skills.
  • Strong foundation in MLOps, with hands-on experience in designing and managing large-scale ML infrastructures.
  • Solid understanding of statistical concepts and algorithms used in machine learning.

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